On the Brink: Predicting Business Failure with Mobile Location-based Checkins.

Decision Support Systems, 2015.

Joseph Pancras (Marketing). Co-authors: Ram Gopal (OPIM), Ramesh Shankar (OPIM), Lei Wang (Penn State University)

Consumers are increasingly using mobile services for engaging with firms in the offline world both directly through purchases and loyalty points redemptions, and indirectly through mobile gamification portals related to the retail outlet. One major such portal is Foursquare, the location-based service provider, which has been gaining popularity in the last few years. This paper, part of a research stream on location based marketing services, studies the question of whether these location based consumer ‘check-ins’ can be a ‘lead indicator’ of business failure of the retail outlet. How can small businesses, like restaurants, use this real-time data to make better-informed business operation decisions in this mobile marketing era? The authors use data collected from Foursquare as well as Yelp to study the predictive power of customer checkins on business failure of restaurants in New York City by using several predictive modeling techniques, such as Neural Network, Logit model and K-nearest neighbor. The customer checkin data from both a focal restaurant and its neighbors have shown strong predictive power on business failure. Compared to the baseline model which uses business characteristic variables to predict failure, incorporating the checkin data captured from location-based services gives a remarkable improvement on predictive accuracy. These findings provide the foundation for future studies on the predictive power of information obtained from location-based services on business operations.

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